{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Coherent Integration\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to Section 6.2.1, coherent integration preserves the phase relationship between the received pulses.  The integration is performed on the complex signals before being passed to the detector, as shown in Figure 6.7.  Coherent integration would ideally result in an increase in the signal-to-noise ratio by a factor of $N$, which is written as (Equation 6.18)\n",
    "\n",
    "$$\n",
    "    {SNR}_{{ci}} = N \\cdot {SNR}_0,\n",
    "$$\n",
    "\n",
    "However, integration loss always occurs in practical systems and this ideal value is not achieved.  Integration loss may be due to component instability, environmental changes, target and platform motion, and fluctuations in target scattering.  Coherent integration is generally not employed over long time frames, especially for targets with quickly changing radar cross section or in the case of correlated clutter. Ideally, coherent integration may be achieved through direct summation of the signals.  However, a more practical approach is through the use of the Fourier transform to determine the unknown initial phase.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting the library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the signal to noise ration (dB), the probability of false alarm, and the number of pulses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace\n",
    "\n",
    "\n",
    "snr_db = [-4.0, 20.0]\n",
    "\n",
    "snr = 10.0 ** (linspace(snr_db[0], snr_db[1], 200) / 10.0)\n",
    "\n",
    "pfa = 1e-9\n",
    "\n",
    "number_of_pulses = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the target type (Swerling 0, Swerling 1, or Swerling 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_type = 'Swerling 1'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the probability of detection using the `probability_of_detection` routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.detection.coherent_integration import probability_of_detection\n",
    "\n",
    "pd = probability_of_detection(snr, number_of_pulses, pfa, target_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the probability of detection for coherent detection using the `matplotlib` routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from numpy import log10\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "plt.plot(10.0 * log10(snr), pd, '')\n",
    "\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Coherent Integration', size=14)\n",
    "\n",
    "plt.xlabel('Signal to Noise (dB)', size=12)\n",
    "\n",
    "plt.ylabel('Probability of Detection', size=12)\n",
    "\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "plt.grid(linestyle=':', linewidth=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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